The Cauchy problem in mathematical General Relativity

Prerequisite Graduate level knowledge in differential geometry and in Riemannian geometry, and basic knowledge in partial differential equations and analysis. Introduction This is an introductory

Bi-Hamiltonian structures of integrable many-body models from Poissonreduction | BlMSA Integrable

Abstract We review our results on bi-Hamiltonian structures of trigonometric spin Sutherland models builton collective spin variables. Our basic observation was that the cotangent bundle T'*U(n) an

Gravity on Null Hypersurfaces: Geometry, Dynamics, Symplectica,Quantization | BIMSA-Tsinghua Stri...

Abstract After presenting an intrinsic description of the geometry of null hypersurfaces, we show howEinstein's equations, that is, the Raychaudhuri and Damour eguations, can be recast as

Non-Holomorphic Cycles and Non-BPs Black Branes | BIMSA-Tsinghua String SeminarBIMSA

Speaker Intro Artan Sheshmani is a Professor of pure Mathematics, specialized inAlgebraic geometry, Enumerative and Derived Geometry, and Mathematics ofString Theory. He is a Professor at Beijing

Differential Geometry Seminar | Sharp bounds in a finitary version of Gromov’s polynomial-growth

Abstract： A famous theorem of Gromov states that if a finitely generated group G has polynomial growth (i.e. there is a polynomial p such that the ball of radius n in some Cayley graph of G always

求真书院举行“中华风雅·端午诗韵”主题端午诗会

沙龙预告 | 战胜"考前无力症"，调出高效复习状态

Statistics Seminar | Learning Topic Models: Identifiability and Finite-Sample Analysis

Abstract： Topic models provide a useful text-mining tool for learning, extracting, and discovering latent structures in large text corpora. Although a plethora of methods have been proposed for to...

喜报！清华大学求真书院学子在第十五届丘成桐大学生数学竞赛中斩获佳绩！

第十五届丘成桐大学生数学竞赛顺利落幕！ 本届大赛共评选出 66 个奖项清华大学求真书院学子斩获佳绩获得25个单项奖独揽个人全能金奖、银奖 祝贺获奖选手！ 金奖 清华大学 王梓畅 清华大学 杨亦诚 银奖 清华大学 ...

Scaling Law-based Functional Clustering: A Statistical Model for Compressing Large Multimodal

Abstract Models across different application domains are exploding in size. This disproportionate increase in model sizes will quickly obstruct the sustainability of deep learning. From my personal